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About one-quarter of genetic variants that are associated with autism spectrum disorder (ASD) are due to de novo mutations in protein-coding genes. Brandler et al. wanted to determine whether changes in noncoding regions of the genome are associated with autism. They applied whole-genome sequencing to ∼2600 families with at least one affected child. Children with ASD had inherited structural variants in noncoding regions from their father. Regulatory regions of some specific genes were disrupted among multiple families, supporting the idea that a component of autism risk involves inherited noncoding variation. Science , this issue p. 327 Whole-genome sequencing identifies inherited noncoding variants in families affected by autism spectrum disorder. The genetic basis of autism spectrum disorder (ASD) is known to consist of contributions from de novo mutations in variant-intolerant genes. We hypothesize that rare inherited structural variants in cis-regulatory elements (CRE-SVs) of these genes also contribute to ASD. We investigated this by assessing the evidence for natural selection and transmission distortion of CRE-SVs in whole genomes of 9274 subjects from 2600 families affected by ASD. In a discovery cohort of 829 families, structural variants were depleted within promoters and untranslated regions, and paternally inherited CRE-SVs were preferentially transmitted to affected offspring and not to their unaffected siblings. The association of paternal CRE-SVs was replicated in an independent sample of 1771 families. Our results suggest that rare inherited noncoding variants predispose children to ASD, with differing contributions from each parent.

Abstract We examined the contribution of the leukocyte function-associated antigen (LFA-1) in a murine model of lung inflammation determined by exposure to the thermophilic actinomycete Faeni rectivirgula. The exposure generated a large influx of cells in the bronchoalveolar space and in the lung parenchyma, as seen from enhanced numbers of cells recovered by bronchoalveolar lavage (BAL) and histologic scoring of lung lesions. Repeated intranasal exposure to F. rectivirgula also resulted in a significant increase in lung hydroxyproline levels. Histologic analysis showed that alveolar wall inflammation, interstitial swelling and congestion, epithelial cell destruction, and granulomas with occasional epithelioid cells were observed in the lungs of challenged mice. Mice injected with rat antibodies against LFA-1 concomitantly with an antigen challenge showed no reduction in the number of BAL inflammatory cells, but lung fibrosis was significantly reduced by anti-LFA-1 treatment as assessed by lung hydroxyproline levels; parenchymal inflammation and tissue damage were also significantly reduced as seen from morphometric analysis. Anti-LFA-1 treatment of mice with established hypersensitivity pneumonitis was found to significantly reduce the levels of lung hydroxyproline and tissue damage; the numbers of BAL cells remained unaffected. From these results, we conclude that the fibrosis and tissue-damaging reactions in hypersensitivity pneumonitis, but not the alveolitis, are partly dependent on LFA-1-mediated cellular interactions.

Rate equation modelling is performed to simulate D2 and D2+D2+ exposure of the W(110) surface with varying coverage of oxygen atoms (O) from the clean surface up to 0.75 monolayer of O. Density Functional Theory (DFT) calculated energetics are used as inputs for the surface processes and desorption energies are optimized to best reproduce the Thermal Desorption Spectrometry (TDS) experiments obtained for D2 exposure. For the clean surface, the optimized desorption energies (1.10 eV–1.40 eV) are below the DFT ones (1.30 eV–1.50 eV). For the O covered surface, the main desorption peak is reproduced with desorption energies of 1.10 eV and 1.00 eV for 0.50 and 0.75 monolayer of O respectively. This is slightly higher than the DFT predicted desorption energies. In order to simulate satisfactorily the total retention obtained experimentally for D2+D2+ exposure, a sputtering process needs to be added to the model, describing the sputtering of adsorbed species (D atoms) by the incident D ions. The impact of the sputtering process on the shape of the TDS spectra, on the total retention and on the recycling of D from the wall is discussed. In order to better characterize the sputtering process, especially its products and yields, atomistic calculations such as molecular dynamics are suggested as a next step for this study.

Several key mutations in the Spike protein receptor binding domain (RBD) have been identified to influence its affinity for the human Angiotensin-Converting Enzyme 2 (ACE2). Here, we perform a comparative study of the ACE2 binding to the wild type (Wuhan) RBD and some of its variants: Alpha B.1.1.7, Beta B.1.351, Delta B.1.617.2, Kappa B.1.617.1, B.1.1.7 + L452R and Omicron B.1.1.529. Using a coiled-coil mediated tethering approach of ACE2 in a novel surface plasmon resonance (SPR)-based assay, we measured interactions at different temperatures. Binding experiments at 10 °C enhanced the kinetic dissimilarities between the RBD variants and allowed a proper fit to a Langmuir 1:1 model with high accuracy and reproducibility, thus unraveling subtle differences within RBD mutants and ACE2 glycovariants. Our study emphasizes the importance of SPR-based assay parameters in the acquisition of biologically relevant data and offers a powerful tool to deepen our understanding of the role of the various RBD mutations in ACE2 interaction binding parameters.

BackgroundIn patients with acute myocardial infarction (AMI), history of atrial fibrillation (AF) and new onset AF during the early phase may be associated with a worse prognosis. Whether both conditions are associated with similar outcomes is a matter of debate.MethodsWe collected information for all patients with AMI seen in French hospitals between 2010 and 2019. Among 797,212 patients seen with STEMI or NSTEMI, 75,701 (9.5%) had history of AF, and 34,768 (4.4%) had new AF diagnosed between day 1 and day 30 after AMI.ResultsPatients with new AF were older and had more comorbidities than those with no AF but were younger and had less comorbidities than those with history of AF. During follow-up [mean (SD) 1.8 (2.4) years, median (interquartile range) 0.7 (0.1–3.1) years], 163,845 deaths and 30,672 ischemic strokes were recorded. Using Cox multivariable analysis, compared to patients with no AF, history of AF was associated with a higher risk of death during follow-up (adjusted hazard ratio HR 1.17, 95% CI 1.16–1.19) and this was also the case for patients with new AF (adjusted HR 2.11, 2.07–2.15). Both history of AF and new AF were associated with a higher risk of ischemic stroke compared to patients with no AF: adjusted HR 1.19 (1.15–1.23) for history of AF, adjusted HR 1.78 (1.68–1.88) for new AF. New AF was associated with a higher risk of death and of ischemic stroke than history of AF: adjusted HR 1.74 (1.70–1.79) and 1.32 (1.23–1.42), respectively.ConclusionsIn a large and systematic nationwide analysis, AF first recorded in the first 30 days after AMI was independently associated with higher risks of death and ischemic stroke than those in patients with no AF or previously known AF.Graphic abstract

Background The primary objective of this cross-sectional study, conducted in Québec and Bristish Columbia (Canada) between February 2021 and January 2022, was to measure the prevalence of viral RNA in oronasal and rectal swabs and serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amongst cats living in households with at least one confirmed human case. Secondary objectives included a description of potential risk factors for the presence of SARS-CoV-2 antibodies and an estimation of the association between the presence of viral RNA in swabs as well as SARS-CoV-2 antibodies and clinical signs. Oronasal and rectal swabs and sera were collected from 55 cats from 40 households at most 15 days after a human case confirmation, and at up to two follow-up visits. A RT-qPCR assay and an ELISA were used to detect SARS-CoV-2 RNA in swabs and serum SARS-CoV-2 IgG antibodies, respectively. Prevalence and 95% Bayesian credibility intervals (BCI) were calculated, and associations were evaluated using prevalence ratio and 95% BCI obtained from Bayesian mixed log-binomial models. Results Nine (0.16; 95% BCI = 0.08–0.28) and 38 (0.69; 95% BCI = 0.56–0.80) cats had at least one positive RT-qPCR and at least one positive serological test result, respectively. No risk factor was associated with the prevalence of SARS-CoV-2 serum antibodies. The prevalence of clinical signs suggestive of COVID-19 in cats, mainly sneezing, was 2.12 (95% BCI = 1.03–3.98) times higher amongst cats with detectable viral RNA compared to those without. Conclusions We showed that cats develop antibodies to SARS-CoV-2 when exposed to recent human cases, but detection of viral RNA on swabs is rare, even when sampling occurs soon after confirmation of a human case. Moreover, cats with detectable levels of virus showed clinical signs more often than cats without signs, which can be useful for the management of such cases.

BackgroundTargeting ischemic strokes patients at risk of incident atrial fibrillation (AF) for prolonged cardiac monitoring and oral anticoagulation remains a challenge. Clinical risk scores have been developed to predict post-stroke AF with suboptimal performances. Machine learning (ML) models are developing in the field of AF prediction and may be used to discriminate post-stroke patients at risk of new onset AF. This study aimed to evaluate ML models for the prediction of AF and to compare predictive ability to usual clinical scores.MethodsBased on a French nationwide cohort of 240,459 ischemic stroke patients without AF at baseline from 2009 to 2012, ML models were trained on a train set and the best model was selected to be evaluate on the test set. Discrimination of the best model was evaluated using the C index. We finally compared our best model with previously described clinical scores.ResultsDuring a mean follow-up of 7.9 ± 11.5 months, 14,095 patients (mean age 77.6 ± 10.6; 50.3% female) developed incident AF. After training, the best ML model selected was a deep neural network with a C index of 0.77 (95% CI 0.76–0.78) on the test set. Compared to traditional clinical scores, the selected model was statistically significantly superior to the CHA2DS2-VASc score, Framingham risk score, HAVOC score and C2HEST score (P < 0.0001). The ability to predict AF was improved as shown by net reclassification index increase (P < 0.0001) and decision curve analysis.ConclusionsML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores.Graphic AbstractAF: atrial fibrillation; DNN: deep neural network; IS: ischemic stroke; KNN: K-nearest neighbors; LR: logistic regression; RFC: random forest classifier; XGBoost: extreme gradient boosting

Background There remain uncertainties regarding diabetes mellitus and the incidence of atrial fibrillation (AF), in relation to type of diabetes, and the interactions with sex and age. We investigated whether diabetes confers higher relative rates of AF in women compared to men, and whether these sex-differences depend on type of diabetes and age. Methods All patients aged ≥ 18 seen in French hospitals in 2013 with at least 5 years of follow-up without a history of AF were identified and categorized by their diabetes status. We calculated overall and age-dependent incidence rates, hazard ratios, and women-to-men ratios for incidence of AF in patients with type 1 and type 2 diabetes (compared to no diabetes). Results In 2,921,407 patients with no history of AF (55% women), 45,389 had prevalent type 1 diabetes and 345,499 had prevalent type 2 diabetes. The incidence rates (IRs) of AF were higher in type 1 or type 2 diabetic patients than in non-diabetics, and increased with advancing age. Among individuals with diabetes, the absolute rate of AF was higher in men than in women. When comparing individuals with and without diabetes, women had a higher adjusted hazard ratio (HR) of AF than men: adjusted HR 1.32 (95% confidence interval 1.27–1.37) in women vs. 1.12(1.08–1.16) in men for type 1 diabetes, adjusted HR 1.17(1.16–1.19) in women vs. 1.10(1.09–1.12) in men for type 2 diabetes. Conclusion Although men have higher absolute rates for incidence of AF, the relative rates of incident AF associated with diabetes are higher in women than in men for both type 1 and type 2 diabetes.

Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores. We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019. Three machine learning models were trained to predict mortality within the first year using a training set (70% of the cohort). The best model was selected to be evaluated and compared with previously published scores on the validation set (30% of the cohort). Discrimination of the best model was evaluated using the C index. Within the first year following atrial fibrillation diagnosis, 342,005 patients (14.4%) died after a period of 83 (SD 98) days (median 37 [10-129]). The best machine learning model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the validation set. Compared to clinical risk scores, the selected model was superior to the CHA2DS2-VASc and HAS-BLED risk scores and superior to dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following atrial fibrillation diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. P < .0001). Machine learning algorithms predict early death after atrial fibrillation diagnosis and may help clinicians to better risk stratify atrial fibrillation patients at high risk of mortality. [Display omitted] CCI, Charlson Comorbidity Index; DNN, deep neural network; HFRS, Hospital Frailty Risk Score.